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Related Concept Videos

Protein Folding01:22

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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Related Experiment Video

Updated: May 17, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Spatial morphoproteomic features predict disease states from tissue architectures.

Thomas Hu1,2, Efe Ozturk1,2, Mayar Allam1

  • 1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.

Iscience
|August 18, 2025
PubMed
Summary
This summary is machine-generated.

SNOWFLAKE, a graph neural network pipeline, predicts disease status by analyzing immune cell organization and morphology in tissue microenvironments. This method accurately classifies infection status and reveals disease-associated cellular patterns.

Keywords:
ImmunologyMachine learningProteomics

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Area of Science:

  • Spatial systems biology
  • Computational pathology
  • Immunology

Background:

  • Understanding immune cell organization in tissue microenvironments is crucial for interpreting disease.
  • Spatial proteomics data offers insights into cellular interactions and disease states.

Purpose of the Study:

  • To introduce SNOWFLAKE, a novel graph neural network pipeline for disease status prediction using spatial proteomics data.
  • To integrate single-cell protein expression and morphological features for enhanced analysis of tissue microenvironments.

Main Methods:

  • Developed SNOWFLAKE, a graph neural network pipeline integrating single-cell protein expression and morphology.
  • Applied SNOWFLAKE to a pediatric COVID-19 dataset for infection status classification.
  • Incorporated morphology into graph edge features to identify spatially organized subgraphs.

Main Results:

  • SNOWFLAKE outperformed conventional machine learning and deep learning methods in classifying COVID-19 infection status.
  • Identified distinct, spatially organized subgraphs associated with disease status in lymphoid follicles.
  • Revealed interpretable cellular motifs within single-cell neighborhoods.

Conclusions:

  • SNOWFLAKE effectively extracts meaningful subgraph embeddings for understanding immune architecture alterations in disease.
  • The approach demonstrates generalizability across different tissue types, including breast cancer and tertiary lymphoid structures.
  • SNOWFLAKE shows significant utility for spatial systems biology and biomarker discovery from multiplex imaging data.